{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:43:47Z","timestamp":1792593827064,"version":"4.5.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,20]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Serum prostate-specific antigen (PSA) is widely used for prostate cancer screening. While the genetics of PSA levels have been studied to enhance screening accuracy, the genetic basis of PSA velocity, the rate of PSA change over time, remains unclear. The Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, a large, randomized study with longitudinal PSA data (15,260 cancer-free males, averaging 5.34 samples per subject) and genome-wide genotype data, provides a unique opportunity to estimate PSA velocity heritability. We developed a mixed model to jointly estimate the heritability of PSA levels at age 54 and PSA velocity. To accommodate the large dataset, we implemented 2 efficient computational approaches: a partitioning and meta-analysis strategy using average information restricted maximum likelihood (AI-REML) and a fast restricted Haseman\u2013Elston (REHE) regression method. Simulations showed that both methods yield unbiased estimates of both heritability metrics, with AI-REML providing smaller variability in the estimation of velocity heritability than REHE. Applying AI-REML to PLCO data, we estimated heritability at 0.32 (s.e. = 0.07) for baseline PSA and 0.45 (s.e. = 0.18) for PSA velocity. These findings reveal a substantial genetic contribution to PSA velocity, supporting future genome-wide studies to identify variants affecting PSA dynamics and improve PSA-based screening.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxag015","type":"journal-article","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:02:11Z","timestamp":1784358131000},"source":"Crossref","is-referenced-by-count":0,"title":["Estimating the heritability of longitudinal rate-of-change: genetic insights into PSA velocity in prostate cancer-free individuals"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9332-4917","authenticated-orcid":false,"given":"Pei","family":"Zhang","sequence":"first","affiliation":[{"name":"National Cancer Institute Division of Cancer Epidemiology and Genetics, , 9609 Medical Center Drive , Rockville, MD 20850,","place":["United States"]},{"name":"University of Maryland, College Park Department of Mathematics, , 4176 Campus Drive , College Park, MD 20740,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5143-6909","authenticated-orcid":false,"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"National Cancer Institute Division of Cancer Epidemiology and Genetics, , 9609 Medical Center Drive , Rockville, MD 20850,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8606-4707","authenticated-orcid":false,"given":"Jianxin","family":"Shi","sequence":"additional","affiliation":[{"name":"National Cancer Institute Division of Cancer Epidemiology and Genetics, , 9609 Medical Center Drive , Rockville, MD 20850,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul S","family":"Albert","sequence":"additional","affiliation":[{"name":"National Cancer Institute Division of Cancer Epidemiology and Genetics, , 9609 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